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  1.  43
    Multispectral Coherence.Fangyu Li, Jie Qi, Bin Lyu & Kurt J. Marfurt - 2018 - Interpretation: SEG 6 (1):T61-T69.
    Seismic coherence is a routine measure of seismic reflection similarity for interpreters seeking structural boundary and discontinuity features that may be not properly highlighted on original amplitude volumes. One mostly wishes to use the broadest band seismic data for interpretation. However, because of thickness tuning effects, spectral components of specific frequencies can highlight features of certain thicknesses with higher signal-to-noise ratio than others. Seismic stratigraphic features may be buried in the full-bandwidth data, but can be “lit up” at certain spectral (...)
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  2.  55
    Semisupervised Multiattribute Seismic Facies Analysis.Jie Qi, Tengfei Lin, Tao Zhao, Fangyu Li & Kurt Marfurt - 2016 - Interpretation: SEG 4 (1):SB91-SB106.
    One of the key components of traditional seismic interpretation is to associate or “label” a specific seismic amplitude package of reflectors with an appropriate seismic or geologic facies. The object of seismic clustering algorithms is to use a computer to accelerate this process, allowing one to generate interpreted facies for large 3D volumes. Determining which attributes best quantify a specific amplitude or morphology component seen by the human interpreter is critical to successful clustering. Unfortunately, many patterns, such as coherence images (...)
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  3.  25
    Attribute Expression of Fault-Controlled Karst — Fort Worth Basin, Texas: A Tutorial.Jie Qi, Bo Zhang, Huailai Zhou & Kurt Marfurt - 2014 - Interpretation: SEG 2 (3):SF91-SF110.
    Much of seismic interpretation is based on pattern recognition, such that experienced interpreters are able to extract subtle geologic features that a new interpreter may easily overlook. Seismic pattern recognition is based on the identification of changes in amplitude, phase, frequency, dip, continuity, and reflector configuration. Seismic attributes, which providing quantitative measures that can be subsequently used in risk analysis and data mining, partially automate the pattern recognition problem by extracting key statistical, geometric, or kinematic components of the 3D seismic (...)
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  4.  24
    The Thickness Imaging of Channels Using Multiple-Frequency Components Analysis.Yan Ye, Bo Zhang, Niu Cong, Jie Qi & Huailai Zhou - 2019 - Interpretation 7 (1):B1-B8.
    Blending of different frequency components of seismic traces is a common way to estimate the relative time thickness of the formation. Red, blue, and green color blending is one of the most popular blending models in analyzing multiple seismic attributes. Geologists and geophysicist interpreters typically associate low-frequency components with a red color, medium-frequency components with a green color, and high-frequency components with a blue color for the thickness estimation of thin beds using frequency components. However, we found that the same (...)
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  5.  21
    The Thickness Imaging of Channels Using Multiple-Frequency Components Analysis.Yan Ye, Bo Zhang, Cong Niu, Jie Qi & Huailai Zhou - 2019 - Interpretation: SEG 7 (1):B1-B8.
    Blending of different frequency components of seismic traces is a common way to estimate the relative time thickness of the formation. Red, blue, and green color blending is one of the most popular blending models in analyzing multiple seismic attributes. Geologists and geophysicist interpreters typically associate low-frequency components with a red color, medium-frequency components with a green color, and high-frequency components with a blue color for the thickness estimation of thin beds using frequency components. However, we found that the same (...)
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  6.  12
    Multispectral Coherence: Which Decomposition Should We Use?Bin Lyu, Jie Qi, Fangyu Li, Ying Hu, Tao Zhao, Sumit Verma & Kurt J. Marfurt - 2020 - Interpretation 8 (1):T115-T129.
    Seismic coherence is commonly used to delineate structural and stratigraphic discontinuities. We generally use full-bandwidth seismic data to calculate coherence. However, some seismic stratigraphic features may be buried in this full-bandwidth data but can be highlighted by certain spectral components. Due to thin-bed tuning phenomena, discontinuities in a thicker stratigraphic feature may be tuned and thus better delineated at a lower frequency, whereas discontinuities in the thinner units may be tuned and thus better delineated at a higher frequency. Additionally, whether (...)
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  7.  22
    Seismic Time-Frequency Decomposition by Using a Hybrid Basis-Matching Pursuit Technique.Xingjian Wang, Bo Zhang, Fangyu Li, Jie Qi & Bo Bai - 2016 - Interpretation: SEG 4 (2):T239-T248.
    Analyzing the time-frequency features of seismic traces plays an important role in seismic stratigraphy analysis and hydrocarbon detection. The current popular time-spectrum analysis methods include short-time Fourier transform, continuous wavelet transform, S-transform, and matching pursuit, among which MP is the most tolerant of the window/scalar effect. However, current MP algorithms do not consider the interfering effects of seismic events on the estimation of optimal wavelets in each decomposition iteration. The interfered reflection seismic events may result in inaccurate estimation of optimal (...)
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  8.  3
    Improving Fault Delineation Using Maximum Entropy Multispectral Coherence.Bin Lyu, Jie Qi, Saurabh Sinha, Jianjun Li & Kurt J. Marfurt - 2020 - Interpretation 8 (4):T835-T850.
    Knowledge of fault geometry plays an important role in reservoir modeling and characterization. Seismic attributes, such as volumetric dip, coherence, and curvature, provide an efficient and objective tool to extract fault geometry attributes. Traditionally, we use noise-attenuated full-bandwidth seismic data to compute coherence followed by smoothing, sharpening, and skeletonization. However, different stratigraphic reflectors with relatively similar waveforms and amplitudes juxtaposing across a fault will algorithmically appear to be continuous, with the resulting fault image being broken. This leads to pseudo fault (...)
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  9.  16
    Intrinsic Mode Chirp Multicomponent Decomposition with Kernel Sparse Learning for Overlapped Nonstationary Signals Involving Big Data.Haixin Sun, Yongchun Miao & Jie Qi - 2018 - Complexity 2018:1-15.
    We focus on the decomposition problem for nonstationary multicomponent signals involving Big Data. We propose the kernel sparse learning, developed for the T-F reassignment algorithm by the path penalty function, to decompose the instantaneous frequencies ridges of the overlapped multicomponent from a time-frequency representation. The main objective of KSL is to minimize the error of the prediction process while minimizing the amount of training samples used and thus to cut the costs interrelated with the training sample collection. The IFs first (...)
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  10.  2
    Azimuthal Anisotropy Analysis Applied to Naturally Fractured Unconventional Reservoirs: A Barnett Shale Example.Jing Zhang, Jie Qi, Yijin Zeng, Kurt Marfurt & Roger Slatt - 2020 - Interpretation 8 (4):SP13-SP29.
    Studying the seismic responses of velocity and amplitude on wide-/full-azimuth seismic data is now common for unconventional reservoir characterization. Velocity variation with azimuth and amplitude variation with azimuth are two of the most popular tools to map not only the relative intensity and orientation of natural fractures but also the strength and orientation of the maximum horizontal stress SH. We prestack time migrated a wide-azimuth Barnett Shale survey in North Texas into eight azimuths and reduced noise on the gathers using (...)
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